Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers.

Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child's body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child's current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child's growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child's obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.

Optimizing the Medication Distribution Process for Inpatient Units.

Pharmacy robots and automated dispensing cabinets are commonly used to distribute medications to inpatient units efficiently and safely. Decisions regarding the use of these technologies are often made without full knowledge regarding system effects. This paper determines a cost effective and safe way to distribute medications to patients across a hospital system by minimizing the distribution cost and missing dose rate. A mathematical model is formulated which captures key aspects of the pharmacy distribution process to determine a primary pathway to distribute each medication and dose type to each unit. The model focuses on three primary distribution pathways: cart fill via pharmacy robot, cart fill via pharmacy technician, and automated dispensing cabinets. The problem is solved using a complete year of data from the Geisinger Medical Center. The model results demonstrate the trade-off between pharmacy technician and nurse workload and missing dose rates that occur as hospitals move from a centralized pharmacy to automated dispensing cabinets. These results demonstrate the importance of evaluating the labor effort and missing dose rates when determining the best method to distribute medication.

Using Mathematical Modeling to Improve the Emergency Department Nurse-Scheduling Process.

INTRODUCTION: Nurse scheduling within an emergency department can be a very time-consuming process as nursing leadership works to reach sufficient nurse-staffing levels across every day of the schedule while also working to satisfy nurse preferences. METHODS: A mathematical model is formulated to determine nursing shifts to minimize the number of shifts across a day while accounting for staffing level requirements, nurse preferences, and meal breaks. RESULTS: A daily schedule based on nursing shifts was created and used within the self-scheduling process. Implementing the schedule improved nurse-staffing levels while decreasing the time necessary to reconcile the monthly schedule, resulting in the potential to increase nurse satisfaction. DISCUSSION: The emergency department can use mathematical modeling to improve the nurse-scheduling process.

Discovery and characterization of [(cyclopentyl)ethyl]benzoic acid inhibitors of microsomal prostaglandin E synthase-1.

We describe a novel class of acidic mPGES-1 inhibitors with nanomolar enzymatic and human whole blood (HWB) potency. Rational design in conjunction with structure-based design led initially to the identification of anthranilic acid 5, an mPGES-1 inhibitor with micromolar HWB potency. Structural modifications of 5 improved HWB potency by over 1000×, reduced CYP2C9 single point inhibition, and improved rat clearance, which led to the selection of [(cyclopentyl)ethyl]benzoic acid compound 16 for clinical studies. Compound 16 showed an IC80 of 24nM for inhibition of PGE2 formation in vitro in LPS-stimulated HWB. A single oral dose resulted in plasma concentrations of 16 that exceeded its HWB IC80 in both rat (5mg/kg) and dog (3mg/kg) for over twelve hours.

Pharmacological Characterization of a Potent Inhibitor of Autotaxin in Animal Models of Inflammatory Bowel Disease and Multiple Sclerosis.

Autotaxin is a secreted enzyme that catalyzes the conversion of lysophosphatidyl choline into the bioactive lipid mediator lysophosphatidic acid (LPA). It is the primary enzyme responsible for LPA production in plasma. It is upregulated in inflammatory conditions and inhibition of autotaxin may have anti-inflammatory activity in a variety of inflammatory diseases. To determine the role of autotaxin and LPA in the pathophysiology of inflammatory disease states, we used a potent and orally bioavailable inhibitor of autotaxin that we have recently identified, and characterized it in mouse models of inflammation, inflammatory bowel disease (IBD), multiple sclerosis (MS), and visceral pain. Compound-1, a potent inhibitor of autotaxin with an IC50 of ∼2 nM, has good oral pharmacokinetic properties in mice and results in a substantial inhibition of plasma LPA that correlates with drug exposure levels. Treatment with the inhibitor resulted in significant anti-inflammatory and analgesic effects in the carrageenan-induced paw inflammation and acetic acid-induced visceral pain tests, respectively. Compound-1 also significantly inhibited disease activity score in the dextran sodium sulfate-induced model of IBD, and in the experimental autoimmune encephalomyelitis model of MS. In conclusion, the present study demonstrates the anti-inflammatory and analgesic properties of a novel inhibitor of autotaxin that may serve as a therapeutic option for IBD, MS, and pain associated with inflammatory states.

Identification and Characterization of Novel Microsomal Prostaglandin E Synthase-1 Inhibitors for Analgesia.

Prostaglandin (PG) E2 plays a critical role in eliciting inflammation. Nonsteroidal anti-inflammatory drugs and selective inhibitors of cyclooxygenase, which block PGE2 production, have been used as key agents in treating inflammation and pain associated with arthritis and other conditions. However, these agents have significant side effects such as gastrointestinal bleeding and myocardial infarction, since they also block the production of prostanoids that are critical for other normal physiologic functions. Microsomal prostaglandin E2 synthase-1 is a membrane-bound terminal enzyme in the prostanoid pathway, which acts downstream of cyclooxygenase 2 and is responsible for PGE2 production during inflammation. Thus, inhibition of this enzyme would be expected to block PGE2 production without inhibiting other prostanoids and would provide analgesic efficacy without the side effects. In this report, we describe novel microsomal prostaglandin E2 synthase-1 inhibitors that are potent in blocking PGE2 production and are efficacious in a guinea pig monoiodoacetate model of arthralgia. These molecules may be useful in treating the signs and symptoms associated with arthritis.

Identification of potent and selective retinoic acid receptor gamma (RARγ) antagonists for the treatment of osteoarthritis pain using structure based drug design.

A series of triaryl pyrazoles were identified as potent pan antagonists for the retinoic acid receptors (RARs) α, ß and γ. X-ray crystallography and structure-based drug design were used to improve selectivity for RARγ by targeting residue differences in the ligand binding pockets of these receptors. This resulted in the discovery of novel antagonists which maintained RARγ potency but were greater than 500-fold selective versus RARα and RARß. The potent and selective RARγ antagonist LY2955303 demonstrated good pharmacokinetic properties and was efficacious in the MIA model of osteoarthritis-like joint pain. This compound demonstrated an improved margin to RARα-mediated adverse effects.

Characterization of 3,3-dimethyl substituted N-aryl piperidines as potent microsomal prostaglandin E synthase-1 inhibitors.

Here we report on novel, potent 3,3-dimethyl substituted N-aryl piperidine inhibitors of microsomal prostaglandin E synthases-1(mPGES-1). Example 14 potently inhibited PGE2 synthesis in an ex vivo human whole blood (HWB) assay with an IC50 of 7nM. In addition, 14 had no activity in human COX-1 or COX-2 assays at 30µM, and failed to inhibit human mPGES-2 at 62.5µM in a microsomal prep assay. These data are consistent with selective mPGES-1-mediated reduction of PGE2. In dog, 14 had oral bioavailability (74%), clearance (3.62mL/(min*kg)) and volume of distribution (Vd,ss=1.6L/kg) values within our target ranges. For these reasons, 14 was selected for further study.

Only adding stationary storage to vaccine supply chains may create and worsen transport bottlenecks.

Although vaccine supply chains in many countries require additional stationary storage and transport capacity to meet current and future needs, international donors tend to donate stationary storage devices far more often than transport equipment. To investigate the impact of only adding stationary storage equipment on the capacity requirements of transport devices and vehicles, we used HERMES (Highly Extensible Resource for Modeling Supply Chains) to construct a discrete event simulation model of the Niger vaccine supply chain. We measured the transport capacity requirement for each mode of transport used in the Niger vaccine cold chain, both before and after adding cold rooms and refrigerators to relieve all stationary storage constraints in the system. With the addition of necessary stationary storage, the average transport capacity requirement increased from 88% to 144% for cold trucks, from 101% to 197% for pickup trucks, and from 366% to 420% for vaccine carriers. Therefore, adding stationary storage alone may worsen or create new transport bottlenecks as more vaccines flow through the system, preventing many vaccines from reaching their target populations. Dynamic modeling can reveal such relationships between stationary storage capacity and transport constraints.

Exploring the known chemical space of the plant kingdom: insights into taxonomic patterns, knowledge gaps, and bioactive regions.

Plants are one of the primary sources of natural products for drug development. However, despite centuries of research, only a limited region of the phytochemical space has been studied. To understand the scope of what is explored versus unexplored in the phytochemical space, we begin by reconstructing the known chemical space of the plant kingdom, mapping the distribution of secondary metabolites, chemical classes, and plants traditionally used for medicinal purposes (i.e., medicinal plants) across various levels of the taxonomy. We identify hotspot taxonomic clades occupied by a large proportion of medicinal plants and characterized secondary metabolites, as well as clades requiring further characterization with regard to their chemical composition. In a complementary analysis, we build a chemotaxonomy which has a high level of concordance with the taxonomy at the genus level, highlighting the close relationship between chemical profiles and evolutionary relationships within the plant kingdom. Next, we delve into regions of the phytochemical space with known bioactivity that have been used in modern drug discovery. While we find that the vast majority of approved drugs from phytochemicals are derived from known medicinal plants, we also show that medicinal and non-medicinal plants do not occupy distinct regions of the known phytochemical landscape and their phytochemicals exhibit properties similar to bioactive compounds. Moreover, we also reveal that only a few thousand phytochemicals have been screened for bioactivity and that there are hundreds of known bioactive compounds present in both medicinal and non-medicinal plants, suggesting that non-medicinal plants also have potential therapeutic applications. Overall, these results support the hypothesis that there are many plants with medicinal properties awaiting discovery.

Impact of introducing the pneumococcal and rotavirus vaccines into the routine immunization program in Niger.

OBJECTIVES: We investigated whether introducing the rotavirus and pneumococcal vaccines, which are greatly needed in West Africa, would overwhelm existing supply chains (i.e., the series of steps required to get a vaccine from the manufacturers to the target population) in Niger. METHODS: As part of the Bill and Melinda Gates Foundation-funded Vaccine Modeling Initiative, we developed a computational model to determine the impact of introducing these new vaccines to Niger's Expanded Program on Immunization vaccine supply chain. RESULTS: Introducing either the rotavirus vaccine or the 7-valent pneumococcal conjugate vaccine could overwhelm available storage and transport refrigerator space, creating bottlenecks that would prevent the flow of vaccines down to the clinics. As a result, the availability of all World Health Organization Expanded Program on Immunization vaccines to patients might decrease from an average of 69% to 28.2% (range = 10%-51%). Addition of refrigerator and transport capacity could alleviate this bottleneck. CONCLUSIONS: Our results suggest that the effects on the vaccine supply chain should be considered when introducing a new vaccine and that computational models can help assess evolving needs and prevent problems with vaccine delivery.

Impact of changing the measles vaccine vial size on Niger's vaccine supply chain: a computational model.

BACKGROUND: Many countries, such as Niger, are considering changing their vaccine vial size presentation and may want to evaluate the subsequent impact on their supply chains, the series of steps required to get vaccines from their manufacturers to patients. The measles vaccine is particularly important in Niger, a country prone to measles outbreaks. METHODS: We developed a detailed discrete event simulation model of the vaccine supply chain representing every vaccine, storage location, refrigerator, freezer, and transport device (e.g., cold trucks, 4 × 4 trucks, and vaccine carriers) in the Niger Expanded Programme on Immunization (EPI). Experiments simulated the impact of replacing the 10-dose measles vial size with 5-dose, 2-dose and 1-dose vial sizes. RESULTS: Switching from the 10-dose to the 5-dose, 2-dose and 1-dose vial sizes decreased the average availability of EPI vaccines for arriving patients from 83% to 82%, 81% and 78%, respectively for a 100% target population size. The switches also changed transport vehicle's utilization from a mean of 58% (range: 4-164%) to means of 59% (range: 4-164%), 62% (range: 4-175%), and 67% (range: 5-192%), respectively, between the regional and district stores, and from a mean of 160% (range: 83-300%) to means of 161% (range: 82-322%), 175% (range: 78-344%), and 198% (range: 88-402%), respectively, between the district to integrated health centres (IHC). The switch also changed district level storage utilization from a mean of 65% to means of 64%, 66% and 68% (range for all scenarios: 3-100%). Finally, accounting for vaccine administration, wastage, and disposal, replacing the 10-dose vial with the 5 or 1-dose vials would increase the cost per immunized patient from $0.47US to $0.71US and $1.26US, respectively. CONCLUSIONS: The switch from the 10-dose measles vaccines to smaller vial sizes could overwhelm the capacities of many storage facilities and transport vehicles as well as increase the cost per vaccinated child.

Structure-based, multi-targeted drug discovery approach to eicosanoid inhibition: Dual inhibitors of mPGES-1 and 5-lipoxygenase activating protein (FLAP).

BACKGROUND: Due to the importance of both prostaglandins (PGs) and leukotrienes (LTs) as pro-inflammatory mediators, and the potential for eicosanoid shunting in the presence of pathway target inhibitors, we have investigated an approach to inhibiting the formation of both PGs and LTs as part of a multi-targeted drug discovery effort. METHODS: We generated ligand-protein X-ray crystal structures of known inhibitors of microsomal prostaglandin E2 synthase-1 (mPGES-1) and the 5-Lipoxygenase Activating Protein (FLAP), with their respective proteins, to understand the overlapping pharmacophores. We subsequently used molecular modeling and structure-based drug design (SBDD) to identify hybrid structures intended to inhibit both targets. RESULTS: This work enabled the preparation of compounds 4 and 5, which showed potent in vitro inhibition of both targets. SIGNIFICANCE: Our findings enhance the structural understanding of mPGES-1 and FLAP's unique ligand binding pockets and should accelerate the discovery of additional dual inhibitors for these two important integral membrane protein drug targets.

Drug Induced Liver Injury (DILI). Mechanisms and Medicinal Chemistry Avoidance/Mitigation Strategies.

Adverse drug reactions (ADRs) are a common cause of attrition in drug discovery and development and drug-induced liver injury (DILI) is a leading cause of preclinical and clinical drug terminations. This perspective outlines many of the known DILI mechanisms and assessment methods used to evaluate and mitigate DILI risk. Literature assessments and retrospective analyses using verified DILI-associated drugs from the Liver Tox Knowledge Base (LTKB) have been used to derive the predictive value of each end point, along with combination approaches of multiple methods. In vitro assays to assess inhibition of the bile salt export pump (BSEP), mitotoxicity, reactive metabolite (RM) formation, and hepatocyte cytolethality, along with physicochemical properties and clinical dose provide useful DILI predictivity. This Perspective also highlights some of the strategies used by medicinal chemists to reduce DILI risk during the optimization of drug candidates.

Identification and Mitigation of Reactive Metabolites of 2-Aminoimidazole-Containing Microsomal Prostaglandin E Synthase-1 Inhibitors Terminated Due to Clinical Drug-Induced Liver Injury.

Two 2-aminoimidazole-based inhibitors, LY3031207 (1) and LY3023703 (2), of the microsomal prostaglandin E synthase-1 (mPGES-1) enzyme were found to cause drug-induced liver injury (DILI) in humans. We studied imidazole ring substitutions to successfully mitigate reactive metabolite (RM) formation. These studies support the conclusion that RM formation may play a role in the observations of DILI and the consideration of 2-aminoimidazoles as structure alerts, due to the high likelihood of bioactivation to generate RMs.

Targeting the Nerve Growth Factor (NGF) Pathway in Drug Discovery. Potential Applications to New Therapies for Chronic Pain.

The neurotrophin nerve growth factor (NGF) has been implicated as a key mediator of chronic pain. NGF binds the tropomysin receptor kinase A (TrkA) and p75, resulting in the activation of downstream signaling pathways that have been linked to pro-nociception. While anti-NGF antibodies have demonstrated analgesia both preclinically and in patients, the mechanism of action of these agents remains unclear. We describe ligands targeting NGF, its receptors, and downstream/related targets. This Perspective highlights large and small molecule approaches to targeting the NGF-TrkA pathway both extra- and intracellularly. In addition, we present a strategic framework for future drug discovery efforts in this pathway beyond the targeting of NGF or its receptors. While existing tools have greatly informed NGF-mediated signaling, ongoing and future pathway research may help focus new drug discovery efforts on key novel targets and mechanisms. This may result in highly differentiated therapeutics with greater efficacy and/or improved safety profiles.

Assessing need for pharmacist involvement to improve care coordination for patients on LAI antipsychotics transitioning from hospital to home: A work system approach.

BACKGROUND: The use of Long-Acting Injectable (LAI) antipsychotic medications has increased for patients with Serious Mental Illness (SMI). Care coordination for this population is complex, and pharmacist involvement may improve and support long-term medication adherence and patient outcomes. OBJECTIVES: (1) Examine pharmacists' role in addressing care coordination and adherence challenges for patients taking Long-Acting Injectable (LAI) antipsychotics; and (2) explore patients' medication use experiences with LAI antipsychotics and educational needs. METHODS: This project utilized a holistic work systems approach to assess the usefulness of implementing a pharmacist-led intervention to improve care coordination for patients taking LAI antipsychotics. Data collection and analyses were guided by the Systems Engineering Initiative for Patient Safety (SEIPS) model. Data were collected using interviews with healthcare team members and patients taking LAI antipsychotics and retrospective chart reviews at a psychiatric hospital in Southwestern Pennsylvania. Data collection elicited information about LAI care coordination, the pharmacist's role, and patients' experiences. Content and thematic analyses were conducted to identify opportunities to improve quality of care and patient outcomes. RESULTS: Sixteen healthcare team members and six patients were interviewed. Twenty patient charts were reviewed to examine the care coordination process. Four themes of the workflow process emerged: pharmacist consultation, in-hospital LAI administration, discharge planning, and outpatient treatment. Key challenges identified included inadequate communication, limited knowledge, and the need for standardized roles. Most patients did not know the name of their LAI antipsychotic and did not recall receiving medication counseling, but were interested in discussing medication concerns with pharmacists. CONCLUSIONS: There is a need for improved communication during LAI care coordination, targeted education for healthcare team members, and standardization of roles. Many patients did not have adequate LAI antipsychotic knowledge or receive appropriate medication counseling. Increased pharmacist involvement in the care coordination process may promote adherence and optimal management of SMI.

Benzopyrans are selective estrogen receptor beta agonists with novel activity in models of benign prostatic hyperplasia.

Benzopyran selective estrogen receptor beta agonist-1 (SERBA-1) shows potent, selective binding and agonist function in estrogen receptor beta (ERbeta) in vitro assays. X-ray crystal structures of SERBA-1 in ERalpha and beta help explain observed beta-selectivity of this ligand. SERBA-1 in vivo demonstrates involution of the ventral prostate in CD-1 mice (ERbeta effect), while having no effect on gonadal hormone levels (ERalpha effect) at 10x the efficacious dose, consistent with in vitro properties of this molecule.

Coverage models to determine outreach vaccination center locations in low and middle income countries.

The Expanded Programme on Immunization (EPI) was established in 1974 to ensure that children all around the world benefit from life-saving vaccines. However, in many low and middle income countries, it is extremely difficult to vaccinate the entire population with the standard regimen of vaccines. One important reason for this is geographically dispersed or nomadic populations. To improve vaccination rates, these countries typically use outreach, where health workers take vaccines to remote locations. Outreach is the last, critical link in the vaccine supply chain, and the locations selected to offer outreach directly impact the number of additional children that can be vaccinated. This research presents four quantitative models that can be used to optimize the selection of outreach locations, in order to maximize the number of residents that can be reached; each model addresses a different type of coverage possibility. The models are analyzed and contrasted using an example with inputs generated from a subset of data from the state of Bihar in India that was made available to the authors.

The impact of implementing a demand forecasting system into a low-income country's supply chain.

OBJECTIVE: To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS: Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS: Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION: The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION: Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements.